Subclass Discriminant Analysis of morphological and textural features for HEp-2 staining pattern classification

Cataldo, Santa Di; Bottino, A.; Islam, Ihtesham Ul; Vieira, Tiago Figueiredo; Ficarra, Elisa

doi:10.1016/j.patcog.2013.09.024

Cited by 47 publications

(28 citation statements)

References 34 publications

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“…In contrast to previous studies that utilized Fisher's LDA for dimensionality reduction and Knearest neighbors (KNN) classifier for classification [24], [25], we employed a Bayesian linear classifier to classify all the subclasses C ij (i = 1, 2, ..., 8; j = 1, 2, 3) and then mapped the subclasses into original classes C ij → C i (i = 1, 2, ..., 8; j = 1, 2, 3).…”

Section: 2) Subclass Classificationmentioning

confidence: 94%

“…2.1) Subclass Divisions: How to divide each class into different subclasses is a crucial problem in SDA. Different unsupervised algorithms have been attempted in this field, including K-means clustering [25], [28], dynamic cluster formation [26], Gaussian mixture model [29], hierarchical clustering [24], nearest neighbor (NN) clustering [22], and valley seeking algorithm [27]. However, these unsupervised methods usually clustered data in terms of their inherent similarity, overlooking the physical meanings of the clustered data.…”

Section: Data Processing and Analysismentioning

confidence: 99%

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Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Yang

Yan

Fang

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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The ability to predict wrist and hand motions simultaneously is essential for natural controls of hand protheses. In this paper, we propose a novel method that includes subclass discriminant analysis (SDA) and principal component analysis for the simultaneous prediction of wrist rotation (pronation/supination) and finger gestures using wearable ultrasound. We tested the method on eight finger gestures with concurrent wrist rotations. Results showed that SDA was able to achieve accurate classification of both finger gestures and wrist rotations under dynamic wrist rotations. When grouping the wrist rotations into three subclasses, about 99.2 ±1.2% of finger gestures and 92.8 ± 1.4% of wrist rotations can be accurately classified. Moreover, we found that the first principal component (PC1) of the selected ultrasound features was linear to the wrist rotation angle regardless of finger gestures. We further used PC1 in an online tracking task for continuous wrist control and demonstrated that a wrist tracking precision (R 2 ) of 0.954 ± 0.012 and a finger gesture classification accuracy of 96.5 ± 1.7% can be simultaneously achieved, with only two minutes of user training. Our proposed simultaneous wrist/hand control scheme is training-efficient and robust, paving the way for musculaturedriven artificial hand control and rehabilitation treatment.Index Terms-Subclass discriminant analysis, principal component analysis, simultaneous wrist/hand control, wearable ultrasound sensing.

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Section: 2) Subclass Classificationmentioning

confidence: 94%

Section: Data Processing and Analysismentioning

confidence: 99%

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Yang

Yan

Fang

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…A general criterion for granule construction is to draw elements with indistinguishability, similarity, proximity or functionality together [28]. Traditional granulation methods adopt unsupervised clustering algorithms, such as Nearest Neighbour (NN) [26], K-Means [29], [30], hierarchical clustering [31], spatial partition trees [32], fuzzy C-means [20], [22], [33], [34], and prototype-based optimization [35], to construct granules. The unsupervised algorithm ensures the elements with certain similarity to be assembled into one granule, however the granules are non-interpretable in the view of semantic context.…”

Section: Attribute-driven Granular Model For Pattern Recognition mentioning

confidence: 99%

Attribute-Driven Granular Model for EMG-Based Pinch and Fingertip Force Grand Recognition

Fang

Zhou

et al. 2021

IEEE Trans. Cybern.

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Fine multi-functional prosthetic hand manipulation requires precise control on the pinch-type and the corresponding force, and it is a challenge to decode both aspects from myoelectric signals. This study proposed an attribute-driven granular model (AGrM) under a machine learning scheme to solve this problem. The model utilises the additionally captured attribute as the latent variable for a supervised granulation procedure. It was fulfilled for EMG-based pinch-type classification and the fingertip force grand prediction. In the experiments, sixteen channels of surface electromyographic signals (i.e. main-attribute) and continuous fingertip force (i.e. sub-attribute) were simultaneously collected while subjects performing eight types of hand pinches. The use of AGrM improved the pinch-type recognition accuracy to around 97.2% by 1.8% when constructing eight granules for each grasping type, and received more than 90% force grand prediction accuracy at any granular level greater than six. Further sensitivity analysis verified its robustness with respect to different channel combination and interferences. In the comparison with other clustering-based granulation methods, AGrM achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.

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“…Based on the same dataset (ICPR 2012 dataset) and experimental protocols, researchers can evaluate their work in a more convincing way. For example, Di Cataldo et al [31] propose a classification approach based on subclass discriminant analysis (SDA) using the integration of morphological, global, and local texture features. It obtains an accuracy of 72.2%.…”

Section: Related Workmentioning

confidence: 99%

Automated classification for HEp-2 cells based on linear local distance coding framework

Lin

et al. 2015

J Image Video Proc.

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The occurrence of antinuclear antibodies (ANAs) in patient serum has significant relation to some specific autoimmune diseases. Indirect immunofluorescence (IIF) on human epithelial type 2 (HEp-2) cells is the recommended methodology for detecting ANAs in clinic practice. However, the currently practiced manual detection system suffers from serious problems due to subjective evaluation. In this paper, we present an automated system for HEp-2 cells classification. We adopt a bag-of-words (BoW) framework which has shown impressive performance in image classification tasks because it can obtain discriminative and effective image representation. However, the information loss is inevitable in the coding process. Therefore, we propose a linear local distance coding (LLDC) method to capture more discriminative information. Our LLDC method transforms original local feature to more discriminative local distance vector by searching for local nearest few neighbors of the local feature in the class-specific manifolds. The obtained local distance vector is further encoded and pooled together to get salient image representation. The LLDC method is combined with the traditional coding methods to achieve higher classification accuracy. Incorporated with a linear support vector machine classifier, our proposed method demonstrated its effectiveness on two public datasets, namely, the International Conference on Pattern Recognition (ICPR) 2012 dataset and the International Conference on Image Processing (ICIP) 2013 training dataset. Experimental results show that the LLDC framework can achieve superior performance to the state-of-the-art coding methods for staining pattern classification of HEp-2 cells.

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Subclass Discriminant Analysis of morphological and textural features for HEp-2 staining pattern classification

Cited by 47 publications

References 34 publications

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

Attribute-Driven Granular Model for EMG-Based Pinch and Fingertip Force Grand Recognition

Automated classification for HEp-2 cells based on linear local distance coding framework

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